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Agentic AI platform with a coding desktop app, CLI, and cloud agents for autonomous software development and office work.
AI content filter and parental-control app that strips explicit material from sites, social, and AI chatbots without full blocking.
AML and fraud compliance platform pairing transaction monitoring, screening, and explainable AI agents for financial institutions.
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
No public pricing
Free trial available
Free trial available
No public pricing
No public pricing
- ✦Multi-agent collaboration for end-to-end tasks
- ✦Persistent memory and custom rules
- ✦Extensible skills and plugins
- ✦Rich context across code, images, and directories
- ✦Automatic codebase documentation generation
- ✦Terminal-native CLI and JetBrains IDE plugin
- ✦Cloud-hosted agents for enterprise use
- ✦Real-time explicit-content filtering
- ✦AI chatbot filtering (ChatGPT, Gemini, Claude, Grok)
- ✦Sexting detection and alerts
- ✦Screen-time limits and app blocking
- ✦Removal-prevention anti-tampering
- ✦Location alerts and social monitoring
- ✦Real-time transaction monitoring and rule engine
- ✦Explainable AI forensics agents
- ✦Dynamic risk scoring
- ✦Watchlist/sanctions/PEP screening
- ✦AI-native case management
- ✦Automated SAR filing to FinCEN and 70+ GoAML countries
- ✦Experiment tracking and visualization for ML training runs
- ✦Model and artifact versioning and management
- ✦Hyperparameter optimization tooling
- ✦Collaborative dashboards and reports for ML teams
- ✦LLM application tracing and evaluation tooling
- →Autonomous feature development in large codebases
- →Terminal-based AI pair programming
- →Cross-department task automation for legal, finance, HR
- →Onboarding developers to unfamiliar codebases
- →Protect children from explicit content online
- →Prevent sexting on a child's device
- →Set healthy screen-time boundaries
- →Personal content filtering for adults
- →AML compliance and monitoring
- →Reducing false-positive alerts
- →Streamlining fincrime investigations and SAR filing
- →ML engineers tracking and comparing training experiments
- →Research teams versioning datasets and model checkpoints
- →Teams building and evaluating LLM-powered applications
- →Organizations collaborating on machine learning projects